The striking proliferation of sensing technologies that provide high-fidelity data streams extracted from every game, induced an amazing evolution of football statistics. Nowadays professional statistical analysis firms like ProZone and Opta provide data to football clubs, coaches and leagues, who are starting to analyze these data to monitor their players and improve team strategies. Standard approaches in evaluating and predicting team performance are based on history-related factors such as past victories or defeats, record in qualification games and margin of victory in past games. In contrast with traditional models, in this paper we propose a model based on the observation of players` behavior on the pitch. We model a the game of a team as a network and extract simple network measures, showing the value of our approach on predicting the outcomes of a long-running tournament such as Italian major league
© Copyright 2015 Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2015 workshop. Published by Department of Computer Science, KU Leuven. All rights reserved.